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作者投稿 专家审稿 编辑办公 编委办公 主编办公

冰川冻土 ›› 2022, Vol. 44 ›› Issue (5): 1470-1481.doi: 10.7522/j.issn.1000-0240.2022.0009

• 冰工程专栏 • 上一篇    


刘斌1(), 冀鸿兰1(), 翟涌光1, 张宝森2, 郜国明2   

  1. 1.内蒙古农业大学 水利与土木建筑工程学院,内蒙古 呼和浩特 010018
    2.黄河水利委员会 黄河水利科学研究院,河南 郑州 450003
  • 收稿日期:2021-09-10 修回日期:2022-02-21 出版日期:2022-10-25 发布日期:2022-11-05
  • 通讯作者: 冀鸿兰 E-mail:liubin_rs@163.com;honglanji@sina.com
  • 作者简介:刘斌,硕士研究生,主要从事河冰遥感监测研究. E-mail: liubin_rs@163.com
  • 基金资助:

Ice thickness inversion and ice storage estimation of Yellow River based on satellite radar characteristic parameters

Bin LIU1(), Honglan JI1(), Yongguang ZHAI1, Baosen ZHANG2, Guoming GAO2   

  1. 1.College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    2.Yellow River Institute of Hydraulic Research, YRCC, Zhengzhou 450003, China
  • Received:2021-09-10 Revised:2022-02-21 Online:2022-10-25 Published:2022-11-05
  • Contact: Honglan JI E-mail:liubin_rs@163.com;honglanji@sina.com


冰厚是冰凌成因分析及预报的重要基础信息,可为防凌减灾提供重要依据。以黄河内蒙古段包头至头道拐水文站为例,利用Sentinel-1雷达影像结合Sentinel-2光学影像对研究区河冰厚度进行估算,首先对Sentinel-2光学影像进行处理,提取凌汛期前黄河主河道边界;然后对Sentinel-1雷达影像进行处理,提取2个强度信息和4个极化分解参数,分析6个雷达特征参数与河冰厚度的相关性;选择相关性最高的参数,采用统计回归方法建立冰厚反演线性回归模型,模型的调整R2为0.657,验证RMSE为9.82 cm,MRE为13.46%,MAE为8.26 cm;对凌汛期黄河冰厚进行反演,分析冰厚时空变化特征,并估算冰储量,同时讨论了河冰的散射机制。研究证明了主动微波遥感数据在黄河冰厚反演中的可行性,可为黄河内蒙古段防凌减灾提供科学参考。

关键词: Sentinel-1, Sentinel-2, 遥感, 河冰厚度, 冰储量, 黄河内蒙古段


Ice flood is a natural disaster unique to high-latitude rivers, which seriously threatens the safety of river hydraulic structures and the stability of river bank ecosystems. Ice thickness is an important basic information for ice formation analysis, ice condition simulation and forecasting, and it can provide an important basis for ice prevention and disaster mitigation. Whether it is ice prevention or ice utilization, ice thickness is a key parameter and a physical indicator that is difficult to monitor. How to estimate it accurately and effectively has always been the focus and difficulty in river ice research. The Inner Mongolia section of the Yellow River has a cold flood season of up to 4 months each year. The river is meandering and is a key section for prevention and control of floods. The acquisition of its ice thickness information is of great significance to the prevention and mitigation of floods in the Yellow River. This paper aims to use Sentinel-1 radar image combined with Sentinel-2 optical image to estimate the thickness of river ice in the Inner Mongolia section of the Yellow River. Taking the Baotou to Toudaoguai hydrological station in Inner Mongolia as an example, the Sentinel-2 optical image is first processed to extract the boundary of the Yellow River main channel before the ice flood season. Then the Sentinel-1 radar image is processed, 2 intensity information and 4 polarization decomposition parameters are extracted, and the correlation between the 6 radar characteristic parameters and the thickness of the river ice is analyzed. The parameters with the highest correlation were selected, and the linear regression model of ice thickness inversion was established by statistical regression method. The adjusted R2 of the model was 0.657, and the RMSE was verified to be 9.82 cm, MRE was 13.46%, and MAE was 8.26 cm. Inversion of ice thickness during the ice flood season, analysis of the characteristics of temporal and spatial changes of ice thickness, and estimation of ice storage, while discussing the scattering mechanism of river ice. It proves the feasibility of active microwave remote sensing data in the inversion of river ice thickness, and provides a reference for blizzard prevention and disaster reduction in the Inner Mongolia section of the Yellow River.

Key words: Sentinel-1, Sentinel-2, remote sensing, river ice thickness, ice storage, Inner Mongolia section of the Yellow River


  • P343.6+3